High Fidelity Probabilistic Structural Health Monitoring

高保真概率结构健康监测

• 批准号: 1563364
• 负责人: Andrew Smyth
• 金额: $ 34.27万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563364&HistoricalAwards=false
• 关键词: Fidelity; Probabilistic; Structural; Health; Monitoring

In the last decade, sensor based Structural Health Monitoring has become an important area of research as it shows great potential for life-safety and economic benefits for improved and responsible management of our aging civil infrastructure. Structural health monitoring involves the detection of damage or deterioration within a structure, the identification of its location and severity and ultimately it can assist in providing a prognosis for the future life of a structure. Many important civil structural systems are large and complex, with many joints and components. The task of identifying damage and deterioration in a high fidelity model of such a large system based on relatively few sensor measurements which themselves are not perfect is highly challenging. The main goal of this research project is to provide more detailed and accurate estimates of structural condition in a probabilistic format, which can be integrated into more reliable decision-making tools for infrastructure stakeholders. Furthermore, development of novel algorithmic tools for nonlinear, high dimensional system identification and parameter learning are of utmost interest in many fields of engineering, biology and even finance. Thus, this research could not only benefit from knowledge in other fields, but could contribute to research domains well beyond the structural monitoring context at the heart of the motivation here. Within Bayesian estimation, the core framework adopted in this research, the algorithmic frontiers lie in the sensitivity to noise, Gaussian versus non-Gaussian, for example, as well as tackling high dimensional problems with local nonlinearities and many static parameters to be identified. Overcoming these challenges requires both a deep understanding of well accepted algorithms as well as development of enhanced or novel algorithmic tools. In Bayesian estimation, two main filtering algorithms are extensively studied in the literature. The consequences of the Gaussianity assumption used in the unscented Kalman filter will be carefully studied on systems of interest, while enhancements of the particle filter, which behaves poorly in high dimensional systems, will be introduced. Strategies to tackle the high dimensionality issue are based on the Rao-Blackwellisation principle as well as partitioning schemes. Off-line algorithms could also be integrated in this framework to obtain more accurate estimates with lower uncertainties. Finally these schemes should be validated on realistic, experimental data.

在过去的十年中，基于传感器的结构健康监测已成为重要的研究领域，因为它显示出具有生命安全和经济利益的巨大潜力，可改善我们老龄化的民用基础设施。结构性健康监测涉及检测结构内的损害或恶化，其位置和严重性的识别，最终可以帮助为结构的未来生活提供预后。许多重要的民用结构系统是大而复杂的，具有许多关节和组件。基于相对较少的传感器测量本身并不完美的传感器测量值，在如此大的系统的高保真模型中识别损害和恶化的任务是高度挑战。该研究项目的主要目标是以概率格式提供更详细和准确的结构状况估计，该概率可以集成到基础架构利益相关者的更可靠的决策工具中。此外，在许多工程，生物学甚至金融领域，用于非线性，高维系统识别和参数学习的新型算法工具的开发都是最大的兴趣。因此，这项研究不仅可以从其他领域的知识中受益，而且可以为研究领域贡献远远超出这里动机核心的结构监测环境。在贝叶斯估计中，这项研究中采用的核心框架是对噪声的敏感性，高斯与非高斯的敏感性，例如，与局部非线性和许多静态参数解决高维问题。克服这些挑战需要对公认的算法以及增强或新颖的算法工具的开发有深刻的了解。在贝叶斯估计中，文献中对两种主要的过滤算法进行了广泛的研究。将仔细研究了无意义的卡尔曼滤波器中使用的高斯假设的后果，而在高维系统中的粒子滤清器的增强（粒子过滤器的增强）将被引入。解决高维问题的策略是基于Rao-Blackwellisation原则和分区方案。离线算法也可以在此框架中集成，以获得更准确的估计值，但不确定性较低。最后，这些方案应在现实的实验数据上进行验证。